if (require("rstanarm") && require("brms")) {
  context("describe_posterior")

  test_that("describe_posterior", {
    set.seed(333)

    # Numeric
    x <- distribution_normal(1000)
    rez <- testthat::expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all"))
    testthat::expect_equal(dim(rez), c(1, 19))
    testthat::expect_equal(colnames(rez), c("Parameter", "Median", "MAD", "Mean", "SD", "MAP", "CI", "CI_low",
                                            "CI_high", "p_map", "pd", "p_ROPE", "ps", "ROPE_CI", "ROPE_low",
                                            "ROPE_high", "ROPE_Percentage", "ROPE_Equivalence", "BF"))
    rez <- testthat::expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9)))
    testthat::expect_equal(dim(rez), c(2, 19))
    rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method = "quantile")
    testthat::expect_equal(dim(rez), c(1, 4))

    # Dataframes
    x <- data.frame(replicate(4, rnorm(100)))
    rez <- testthat::expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all"))
    testthat::expect_equal(dim(rez), c(4, 19))
    rez <- testthat::expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9)))
    testthat::expect_equal(dim(rez), c(8, 19))
    rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method = "quantile")
    testthat::expect_equal(dim(rez), c(4, 4))

    # Rstanarm
    library(rstanarm)
    x <- insight::download_model("stanreg_lm_1")
    rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all")
    testthat::expect_equal(dim(rez), c(2, 21))
    testthat::expect_equal(colnames(rez), c("Parameter", "Median", "MAD", "Mean", "SD", "MAP", "CI", "CI_low",
                                            "CI_high", "p_MAP", "pd", "p_ROPE", "ps", "ROPE_CI", "ROPE_low",
                                            "ROPE_high", "ROPE_Percentage", "ROPE_Equivalence", "BF", "Rhat",
                                            "ESS"))
    rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9))
    testthat::expect_equal(dim(rez), c(4, 21))
    rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method = "quantile", diagnostic = NULL, priors = FALSE)
    testthat::expect_equal(dim(rez), c(2, 4))

    # Brms
    library(brms)
    x <- insight::download_model("brms_mixed_1")
    rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, ci = c(0.8, 0.9))
    testthat::expect_equal(dim(rez), c(4, 16))
    testthat::expect_equal(colnames(rez), c("Parameter", "Median", "MAD", "Mean", "SD", "MAP", "CI", "CI_low",
                                            "CI_high", "pd", "ROPE_CI", "ROPE_low", "ROPE_high", "ROPE_Percentage",
                                            "ESS", "Rhat"))
    rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method = "quantile", diagnostic = NULL)
    testthat::expect_equal(dim(rez), c(2, 4))


    # BayesFactor
    # library(BayesFactor)
    # x <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
    # rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all")
    # testthat::expect_equal(dim(rez), c(4, 16))
    # rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9))
    # testthat::expect_equal(dim(rez), c(8, 16))
    # rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method="quantile")
    # testthat::expect_equal(dim(rez), c(4, 4))
  })


  if (require("insight")) {
    m <- insight::download_model("stanreg_merMod_5")
    p <- insight::get_parameters(m, effects = "all")

    test_that("describe_posterior", {
      testthat::expect_equal(
        describe_posterior(m, effects = "all")$Median,
        describe_posterior(p)$Median,
        tolerance = 1e-3
      )
    })

    m <- insight::download_model("brms_zi_3")
    p <- insight::get_parameters(m, effects = "all", component = "all")

    test_that("describe_posterior", {
      testthat::expect_equal(
        describe_posterior(m, effects = "all", component = "all")$Median,
        describe_posterior(p)$Median,
        tolerance = 1e-3
      )
    })
  }


  test_that("describe_posterior w/ BF+SI", {
    testthat::skip_on_cran()
    testthat::skip_on_travis()

    x <- insight::download_model("stanreg_lm_1")
    set.seed(555)
    rez <- describe_posterior(x, ci_method = "SI", test = "bf")


    # test si
    set.seed(555)
    rez_si <- si(x)
    testthat::expect_equal(rez$CI_low, rez_si$CI_low, tolerance = 0.1)
    testthat::expect_equal(rez$CI_high, rez_si$CI_high, tolerance = 0.1)

    # test BF
    set.seed(555)
    rez_bf <- bayesfactor_parameters(x)
    testthat::expect_equal(rez$BF, rez_bf$BF, tolerance = 0.1)
  })
}